# Pandas Series.mean() Function

The Pandas `Series.mean()` method is used to calculate the mean or average of the values. It returns a float value representing the mean of the series. In this article, I will explain the syntax of Series.mean() function, its parameters, and how to compute the mean values of a given Series object with examples.

## 1. Syntax of Series.mean() Function

Following is the syntax of creating Series.mean() function.

``````
# Syntax of Series.mean() function
Series.mean(axis=_NoDefault.no_default, skipna=True, level=None, numeric_only=None, **kwargs)
``````

Following are the parameters of the mean().

• `axis` – {index (0)}: Specify the axis for the function to be applied on. For Series, this parameter is unused and defaults to 0.
• `skipna` – bool, default True: Excludes all None/NaN from the mean/computing the result. Default set to True
• `level` – Use with multiindex. Takes int or level name, default None
• `numeric_only` – Excludes all non-numeric values. Considers only int, float & boolean. Default None
• `**kwargs` – Additional keyword arguments to be passed to the function.

## 2. Pandas Series mean() Usage

The `mean()` function returns the arithmetic mean of given object elements in Pandas. Arithmetic mean is a sum of elements of given object, along with the specified axis divided by the number of elements.

You can also specify the `axis` parameter to specify the axis along which the mean is calculated. By default, `axis=0`, which means the mean is calculated along the rows (i.e., across all the columns). If you set `axis=1`, the mean is calculated along the columns (i.e., across all the rows).

Now, let’s create pandas series using a list of values.

``````
import pandas as pd
import numpy as np

# Create a Series
ser = pd.Series([13, 25, 6, 10, 12, 9, 20])
print(ser)
``````

The following example calculates the mean.

``````
# Use Series.mean() function
ser2 = ser.mean()
print(ser2)
``````

Yields below output.

``````
# Output:
13.571428571428571
``````

## 3. Series Mean Ignore NaN

By default `skipna=True` meaning it ignores the `NaN` (Not a Number) values when calculating the mean. If a series contains `NaN` values, they are automatically excluded from the calculation.

``````
# Pandas series mean ignore nan
ser = pd.Series([13, 25, None, 10, 12, None, 20, 30, np.nan])
ser2 = ser.mean(skipna = True)
print(ser2)

# Output:
# 18.333333333333332
``````

You can also use the `skipna=False` to not ignore NaN values, and if you have Nan values in the series it returns nan values.

``````
# Pandas series mean ignore nan
ser = pd.Series([13, 25, None, 10, 12, None, 20, 30, np.nan])
ser2 = ser.mean(skipna = False)
print(ser2)

# Output:
# nan
``````

## 4. Complete Example of Series.mean() Function

``````
import pandas as pd
import numpy as np

# Create a Series
ser = pd.Series([13, 25, 6, 10, 12, 9, 20])
print(ser)

# Use Series.mean() function
ser2 = ser.mean()
print(ser2)

# Pandas series mean ignore nan
ser = pd.Series([13, 25, None, 10, 12, None, 20, 30, np.nan])
ser2 = ser.mean(skipna = True)
print(ser2)
``````

## 7. Conclusion

In this article, I have explained the pandas series `mean()` function that returns the mean of values of a given series object with examples.

Happy Learning !!

## References 